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<p>This article proposes an efficient anti-rollover control strategy of heavy vehicles. The method is based on model predictive control (MPC) and adaptive extended state observer (ESO). The designed control algorithm considers three challenges including stability, rapidity and external disturbance. First, MPC method is presented for the vehicle model with multiple constraints. In order to reduce the computational burden, Laguerre function is introduced to decrease the matrix dimension, and an improved primal–dual interior point method is proposed to accelerate the solution of quadratic programming problem. Then, a disturbance compensation scheme is designed to estimate unknown disturbances through ESO, the corresponding parameters are obtained based on improved interval type-2 fuzzy neural network (IT2FNN), further improving the learning efficiency and control accuracy. Finally, the simulation results indicate the designed algorithm can enhance the calculation efficiency and meet the real time requirements, and obviously increase the tracking accuracy and robustness performance.</p>

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Fast model predictive control of heavy vehicle anti-rollover based on extended state observer with interval type-2 fuzzy neural network

  • 期刊论文

  • 发表日期：

    2026年2月24日

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  • Hongwei Wang,
  • Shiyi Feng,
  • Meixi Zhao,
  • Yu Zhang,
  • Qingqing Zhang

摘要

This article proposes an efficient anti-rollover control strategy of heavy vehicles. The method is based on model predictive control (MPC) and adaptive extended state observer (ESO). The designed control algorithm considers three challenges including stability, rapidity and external disturbance. First, MPC method is presented for the vehicle model with multiple constraints. In order to reduce the computational burden, Laguerre function is introduced to decrease the matrix dimension, and an improved primal–dual interior point method is proposed to accelerate the solution of quadratic programming problem. Then, a disturbance compensation scheme is designed to estimate unknown disturbances through ESO, the corresponding parameters are obtained based on improved interval type-2 fuzzy neural network (IT2FNN), further improving the learning efficiency and control accuracy. Finally, the simulation results indicate the designed algorithm can enhance the calculation efficiency and meet the real time requirements, and obviously increase the tracking accuracy and robustness performance.